What hardware do you need to run Llama 3.1 8B Instruct?

Llama 3.1 8B Instruct at Q4_K_M is 4.9 GB of weights, with a context ceiling of 128k tokens. Fine for summaries and light chat. Don’t trust it with maths or multi-step tool use. On the cheapest machine that runs it, a Mac mini M6, 16GB, it generates 12 tok/s at 32k of context, worked out from memory bandwidth.

How good is Llama 3.1 8B Instruct, really?

On the Artificial Analysis Intelligence Index v4.3 it scores 7, which puts it in the Below every hosted tier band. Fine for simple, well-specified tasks. Noticeably less capable than anything the big labs sell today. See the whole table.

• Summarising — good
• Translation — usable
• Everyday coding — usable
• Reasoning & maths — don’t
• Agentic work — don’t

Head to head with its neighbours on the leaderboard: vs Devstral Small 2 24B above it, vs Qwen3 32B below. One more model needs much the same memory at 32k of context: vs Ministral 3 8B.

Is Llama 3.1 8B Instruct still worth running?

Not on the two figures this site can compare. The intelligence index puts seven of the current models here above Llama 3.1 8B Instruct, and not one of them asks more of a machine: at 32k of context it needs 9.2 GB and scores 7, where the strongest of the seven, Qwen3.5 9B, needs 6.8 GB and scores 14 (reasoning mode; 13 without).

The cheapest machine still sold that holds Llama 3.1 8B Instruct at 32k is the Mac mini M6, 16GB at $899, which holds Qwen3.5 9B as well. This site quotes Qwen3.5 9B at 17 tok/s estimated there and Llama 3.1 8B Instruct at 12 tok/s estimated.

Both scores above are Artificial Analysis's own estimate rather than a measured run of the full v4.3 suite.

What Llama 3.1 8B Instruct costs either way

Renting the same model costs $0.02 per million input tokens and $0.04 per million output (OpenRouter, cheapest active endpoint, checked 2026-10-29). Buying a machine only beats that if you use it hard enough, for long enough, that the hardware price divides down below the rental bill. What a million tokens costs each way puts the two prices side by side.

Machines that run Llama 3.1 8B Instruct

Every machine here runs it, but not to the same length. The Mac mini M6, 16GB stops at 32k; the Strix Halo Framework Desktop, 32GB takes it to 128k, this model's own ceiling. The weights are the same size on every machine; what differs is the memory left for the key-value cache, which grows with every token you keep.

All 56 machines on this site run it at 32k, not just the eight in the table. One table sets every machine against the others: price, the memory its GPU can use, what it holds and how long it takes to pay for itself.

One machine per family, cheapest first. Speeds are measured where a public benchmark exists and estimated from memory bandwidth otherwise; the calculator says which for any configuration. The longest context is the longest setting the calculator offers that the machine still holds this model at, cache included, and no machine is shown taking it past its own 128k limit. Each one opens the calculator on that machine at that length.

For scale, the calculator starts from 80 tok/s for a hosted API and times a local machine against it. 1 of the 8 above reaches it, and the slowest is 12 tok/s.

The cheapest machine that runs it is sold with 16 GB. There is a page on what 16 GB runs, machine by machine: it names the 6 machines here sold at that size, and the 15 current models the roomiest of them holds at 32k.

It fits in less memory than that: the smallest size here that runs it at 32k is 12 GB, on the GeForce RTX 3060, 12GB at $329, card only, which hands a model 11 GB and is no longer sold.

Run Llama 3.1 8B Instruct at 128k on the Strix Halo Framework Desktop, 32GB

How much memory does Llama 3.1 8B Instruct need at 128k?

Every figure above is priced at 32k of context. At 128k, this model's own ceiling, Llama 3.1 8B Instruct needs 22.1 GB rather than 9.2 GB. The weights are the same either way; the key-value cache goes from 4.3 GB to 17.2 GB.

All 37 machines here still sold at a published price hold Llama 3.1 8B Instruct at 32k and 28 hold it at 128k. The cheapest still sold goes from the Mac mini M6, 16GB at $899 to the Framework Desktop, 32GB at $1,269.

On the Framework Desktop, 32GB it runs 21 tok/s estimated at 32k and 8.7 tok/s estimated at 128k. Every token generated reads the whole cache back, so a longer window is slower as well as bigger.

What has been measured running Llama 3.1 8B Instruct

Every speed in the table above is worked out from memory bandwidth, and that is not because nobody has run Llama 3.1 8B Instruct. Nine machines here have a published speed for it, and the table above has none of them: six are no longer sold and three are still sold but not the cheapest of their family. They are below, as published and again at 32k of context, which is what every other speed on this page is taken at.

A published figure is what its source reports, at the window the model was run at. The 32k figure is that measurement read at 32k of context: every token generated reads the weights and the whole key-value cache, so the longer the window the slower it runs. A discontinued machine's price is the one it launched at. Where two memory tiers of one machine carry the same figure, the speed is set by the bandwidth of the chip they share rather than by the amount of memory. Each published figure links the source that took it.

Llama 3.1 8B Instruct at Q4_K_M or Q8_0?

Llama 3.1 8B Instruct is one of two models this site lists at two precisions, and the lighter build is the subject of this page. The weights go from 4.9 GB at Q4_K_M to 8.5 GB at Q8_0. The key-value cache is the same size either way, 4.3 GB at 32k of context, because both builds share an architecture, so what a machine has to hold goes from 9.2 GB to 12.8 GB. All 56 machines on this site hold the lighter build at 32k; 50 hold the heavier. The cheapest one still sold is the Mac mini M6, 16GB at $899 for the lighter build and the Mac mini M6, 24GB at $1,099 for the heavier.

The heavier build costs speed as well, because every token generated reads the weights and the whole cache. On the Mac mini M6, 24GB, the cheapest machine still sold that holds both, this site quotes 14 tok/s at Q4_K_M and 9.9 tok/s at Q8_0, both worked out from memory bandwidth.

What the extra precision buys is the half this site has no figure for. The intelligence index has one score for Llama 3.1 8B Instruct, 7, which both builds here carry, and the five ratings above are the same five on both. The memory and the speed are the whole measured difference between them.

The specifics

Parameters

8B

Quantisation

Q4_K_M — also listed here at Q8_0, which is 8.5 GB

Weights on disk

4.9 GB

KV cache

4.3 GB at 32k context. How weights and cache add up.

Maximum context

128k tokens

Licence

Llama 3.1 Community License

Sources

bartowski/Meta-Llama-3.1-8B-Instruct-GGUF, unsloth/Llama-3.1-8B-Instruct